Update app.py

app.py

@@ -264,8 +264,8 @@ def keyphrase_generator(article_link, model_1, model_2, max_num_keywords, model_
   
 
 igen_pubmed = gr.Interface(keyphrase_generator, 
-             inputs=[gr.inputs.Textbox(lines=1, placeholder="Provide article web link here (Can be chosen from examples below)",default="", label="Article web link"),
-                     gr.inputs.Dropdown(choices=['sentence-transformers/all-mpnet-base-v2',
+             inputs=[gr.components.Textbox(lines=1, placeholder="Provide article web link here (Can be chosen from examples below)",default="", label="Article web link"),
+                     gr.components.Dropdown(choices=['sentence-transformers/all-mpnet-base-v2',
                                                  'sentence-transformers/all-mpnet-base-v1',                       
                                                  'sentence-transformers/all-distilroberta-v1',
                                                  'sentence-transformers/gtr-t5-large',
@@ -280,7 +280,7 @@ igen_pubmed = gr.Interface(keyphrase_generator,
                                         type="value", 
                                         default='sentence-transformers/stsb-roberta-base-v2', 
                                         label="Select any SBERT model for TextRank from the list below"),
-                     gr.inputs.Dropdown(choices=['sentence-transformers/paraphrase-mpnet-base-v2',
+                     gr.components.Dropdown(choices=['sentence-transformers/paraphrase-mpnet-base-v2',
                                                  'sentence-transformers/all-mpnet-base-v1',
                                                  'sentence-transformers/paraphrase-distilroberta-base-v1',
                                                  'sentence-transformers/paraphrase-xlm-r-multilingual-v1',
@@ -297,14 +297,14 @@ igen_pubmed = gr.Interface(keyphrase_generator,
                                         type="value", 
                                         default='sentence-transformers/all-mpnet-base-v1', 
                                         label="Select any SBERT model for keyphrases from the list below"),                                                     
-                     gr.inputs.Slider(minimum=5, maximum=20, step=1, default=10, label="Max Keywords"),
-                     gr.inputs.Dropdown(choices=['cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
+                     gr.components.Slider(minimum=5, maximum=20, step=1, default=10, label="Max Keywords"),
+                     gr.components.Dropdown(choices=['cambridgeltl/SapBERT-from-PubMedBERT-fulltext',
                                                  'cambridgeltl/SapBERT-from-PubMedBERT-fulltext-mean-token'],
                                         type="value", 
                                         default='cambridgeltl/SapBERT-from-PubMedBERT-fulltext', 
                                         label="Select any SapBERT model for clustering from the list below"),
-                     gr.inputs.Slider(minimum=5, maximum=15, step=1, default=10, label="PubMed Max Abstracts"),
-                     gr.inputs.Dropdown(choices=['pritamdeka/S-Bluebert-snli-multinli-stsb',
+                     gr.components.Slider(minimum=5, maximum=15, step=1, default=10, label="PubMed Max Abstracts"),
+                     gr.components.Dropdown(choices=['pritamdeka/S-Bluebert-snli-multinli-stsb',
                                                  'pritamdeka/S-BioBert-snli-multinli-stsb',
                                                  'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb',
                                                  'sentence-transformers/all-mpnet-base-v2'],